The price and consumption of energy throughout the world has been increasing dramatically over recent years and is expected to continue along this trend in the years to come. According to the 2008 U.S. Department of Energy Annual Energy Outlook, residential energy consumption is expected to increase by approximately one percent per year for the next 20 years while energy prices slowly rise (See, Annual Energy Outlook, US Department of Energy, http://www.eia.doe.gov/oiaf/aeo/). Residential-related carbon dioxide emissions are also expected to increase. These trends clearly indicate the need for building technology solutions that lessen energy consumption. To achieve this goal, appliances or devices can be replaced with more energy efficient alternatives, building occupants or owners can alter their behavior to reduce the use of energy-consuming devices, or automated building management solutions can control the operation of devices in the building so as to achieve less energy consumption or schedule operation for non-peak demand periods to reduce energy costs. All of these approaches must be based on detailed knowledge of the amount of energy consumed by devices currently in the building and their corresponding periods of operation so that appropriate decisions can be made about how to reduce this consumption. Therefore, measurement is necessary for awareness.
A number of systems exist for measuring energy consumption in a building and reporting this to users. See, for example, D. Parker, D. Hoak, A. Meier, R. Brown, “How much energy are we using? Potential of residential energy demand feedback devices”, Proc. Summer Study on Energy Efficiency in Buildings, 2006. However, these systems typically only report the total amount of electricity consumption for the entire building to the user. To obtain truly detailed information that is most informative for determining how to achieve energy savings, the user must manually switch devices on and off and note the change in the total consumption report. Most current systems lack any disaggregated reporting of the overall consumption and use-patterns of individual devices and appliances. Some exceptions are systems that use separate measurement devices to measure the electricity consumption of, for example, a sub-circuit of the building, an individual wall outlet or even an individual device itself. However, use of multiple metering devices distributed throughout the building to achieve this type of feedback is both costly and cumbersome to install.
As an alternative, the concept of non-intrusive load monitoring is known. See, for example, G. W. Hart, Nonintrusive appliance load monitoring, Proceedings of the IEEE, vol. 80, no. 12, pp. 1870-1891, 1992. See also U.S. Pat. No. 4,858,141, issued to Hart et al. Further improvements have been made in this area, particularly by Leeb. See for example, C. Laughman, K. Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, P. Armstrong, Power signature analysis, IEEE Power and Energy Magazine, vol. 1, no. 2, pp. 56-63, 2003. See also U.S. Pat. No. 5,483,153 (issued to Leeb, et al), U.S. Pat. No. 7,043,380 (issued to Rodenberg, et al), U.S. Pat. No. 6,993,417 (issued to Osann, Jr.), U.S. Pat. No. 5,337,013 (issued to Langer et al), U.S. Pat. No. 5,717,325 (issued to Leeb, et al), and U.S. Pat. No. 6,993,417 (issued to Osann, Jr.). However, applicability and implementation of these previous techniques to real building environments with numerous devices operating in parallel has been minimally studied.
Non-Intrusive Load Monitoring (NILM) derives its name from the fact that, from the perspective of the electric utility company, the technique is able to monitor individual loads in a building without intruding (e.g., placing sensors or other devices) into the customer's property. As described above, this approach, also referred to as NIALM (Non-Intrusive Appliance Load Monitoring), has been studied extensively for the past two decades by researchers around the world, yielding promising results. Although the prior art results obtained so far are or may be applicable to many loads present in modern buildings, the prior art has failed to overcome the main issues that have been keeping the technology in the laboratory rather than being adopted by society: automatic training of the algorithms, effective user feedback, etc. Additionally, there is little research showing test results involving modern real world buildings, and even fewer experimentation addressing the possible energy savings that the approach could bring in the short, medium, or long term. Furthermore, with the ever-more widespread availability of the so-called “Smart Meters”, there is a new opportunity to obtain power metrics without having to install custom hardware in buildings.
Hart [George W. Hart, Jr. Edward C. Kern, and Fred C. Schweppe. U.S. Pat. No. 4,858,141-non-intrusive appliance monitor apparatus, August 1989], [G. W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870-1891, 1992], [G. W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870-1891, 1992.], was one of the first researchers to publish in the area. His early publications described a method for utilizing normalized real and reactive power (P and Q, respectively) measurements from the main electrical feed of a residential building. His technique relied on steady state power metrics (i.e., disregarding any transient, non-stable state) to describe in a distinct way the power draw of most home appliances of the time. In other words, when an individual appliance changed its state from off to on, for example, the change in the total real- and reactive-power of the house would be almost unique for the mentioned appliance. Hart referred to these changes as the appliance's signature, and described methods for correcting possible overlaps in this signature space by making use of appliance state transition models (e.g., an appliance cannot go from off to on and then again to on).
Norford and Leeb improved on Hart's technique by analyzing the startup transients of appliances [Leslie K. Norford and Steven B. Leeb. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings, 24(1):51-64, 1996.] and introducing better algorithms for detecting when state transitions have occurred [Dong Luo, Leslie Norford, Steven Shaw, and Steven Leeb. Monitoring HVAC equipment electrical loads from a centralized location—methods and field test results. ASHRAE Transactions, 108(1):841-857, 2002.]. In [C. Laughman, Kwangduk Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, and P. Armstrong. Power signature analysis. Power and Energy Magazine, IEEE, 1(2):56-63, 2003] investigators describe how the use of current harmonics can improve the process even further, allowing for the detection and classification of certain continuously variable loads. Moreover, [W. Wichakool, A.-T. Avestruz, R. W. Cox, and S. B. Leeb. Resolving power consumption of variable power electronic loads using nonintrusive monitoring. In Power Electronics Specialists Conference, 2007. PESC 2007. IEEE, pages 2765-2771, 2007] presents further improvements to the solution for the problem of variable power electronics by using a spectral estimation method and a switching function technique. A summary and presentation of the latest achievements in this line of work can be found in [S. R. Shaw, S. B. Leeb, L. K. Norford, and R. W. Cox. Nonintrusive load monitoring and diagnostics in power systems. Instrumentation and Measurement, IEEE Transactions on, 57(7):1445-1454, 2008].
Other research has focused on utilizing the technique for monitoring the health of large appliances, by carefully analyzing any changes to its startup transient and associated signature [James Paris. A framework for non-intrusive load monitoring and diagnostics. Thesis, Massachusetts Institute of Technology, 2006. Thesis M. Eng.—Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006], [R. Cox, S. B. Leeb, S. R. Shaw, and L. K. Norford. Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion. In Applied Power Electronics Conference and Exposition, 2006. APEC '06. Twenty-First Annual IEEE, page 7 pp., 2006], [Leslie K. Norford and Steven B. Leeb. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings, 24(1):51-64, 1996]. Efforts have also been made towards eliminating the need to collect current readings by inferring these from pure voltage measurements [R. Cox, S. B. Leeb, S. R. Shaw, and L. K. Norford. Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion. In Applied Power Electronics Conference and Exposition, 2006. APEC '06. Twenty-First Annual IEEE, page 7 pp., 2006]; while others have focused on methods that do not require an appliance to change from one state to the other but rather detect the presence of an appliance while it is being used [D. Srinivasan, W. S. Ng, and A. C. Liew. Neural-network-based signature recognition for harmonic source identification. Power Delivery, IEEE Transactions on, 21(1):398-405, 2006].
There are also a growing number of research projects that have explored different classification algorithms and feature extraction methods. Neural networks have been used by [A. Prudenzi. A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel. In Power Engineering Society Winter Meeting, 2002. IEEE, volume 2, pages 941-946 vol. 2, 2002], and more recently by [Hsueh-Hsien Chang, Ching-Lung Lin, and Hong-Tzer Yang. Load recognition for different loads with the same real power and reactive power in a non-intrusive load-monitoring system. In 12th International Conference on Computer Supported Cooperative Work in Design 2008, pages 1122-1127. IEEE, April 2008]. Genetic algorithms and clustering approaches were applied by [M. Baranski and J. Voss. Genetic algorithm for pattern detection in NIALM systems. In Systems, Man and Cybernetics, 2004 IEEE International Conference on, volume 4, pages 3462-3468 vol. 4, The Hague, The Netherlands, 2004. IEEE] to data acquired from utility meters using an optical sensor. A rule based system was developed by [Linda Farinaccio and Radu Zmeureanu. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings, 30(3):245-259, August 1999] to solve the disaggregation problem. An attempt to create a general taxonomy for appliance signatures is presented in [H. Y. Lam, G. S. K. Fung, and W. K. Lee. A novel method to construct taxonomy electrical appliances based on load signatures of. Consumer Electronics, IEEE Transactions on, 53(2):653-660, 2007], where using clustering techniques and a novel feature set the researchers found common traits in the signatures of same-type appliances present in modern residential buildings.
Despite almost two decades of research in the area, techniques for non-intrusively disaggregating the total electrical load of buildings remain in the hands of researchers and have not yet been adopted by society in general. Even though the list of publications in the field is currently large, and still growing, the number of commercial applications of NILM is close to null. To date, the only commercially available line of products that makes use of the technique, in some extent, is developed by Enetics [Enetics, Inc. Enetics, Inc. (viewed Apr. 22, 2009). http://www.enetics.com]. These products are based on one of Hart's patents [George W. Hart, Jr. Edward C. Kern, and Fred C. Schweppe. U.S. Pat. No. 4,858,141—non-intrusive appliance monitor apparatus, August 1989.].
However, for a number of reasons the prior art has not yet reached wide adoption. One such reason is the fact that previous approaches have relied on custom hardware to monitor the power lines, which has only recently become inexpensive. Additionally, the level of unwanted noise present in modern building's electrical distribution system also makes the task more difficult. Attempts to detect events (e.g., appliance state transitions) in these settings prove to be much harder. Furthermore, the field of Machine Learning has progressed significantly in the past few years yielding more powerful algorithms that are yet to be explored in this context. As with signal processing techniques, all these algorithms can now be run on less expensive and more powerful computing platforms than those available in the past.
Training the algorithms has also been an obstacle for wide adoption. In order for the algorithms to learn how to correctly classify signatures of appliance state transitions, a number of examples need to be presented to them. This training process needs to be designed around the user, with the goal of providing an easy and simple experience. Manual training, by which the users switch appliances on and off and then provide a label for the state transition, should be used as a last resource and only when other approaches fail. The present invention includes methods for improving training, as will be described in more detail later in this document.
It is also worth noting that, with a few exceptions, such as [Linda Farinaccio and Radu Zmeureanu. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings, 30(3):245-259, August 1999] who utilized the time of day in their rule-based system, the vast majority of the previous work has focused on acquiring information from the power lines.
Another reason that this technology has not progressed from research to development may be the relatively low cost of electricity and consequent lack of interest in detailed measurement of its consumption. However, the growing body of research on the conservation effects of energy-use feedback is now starting to include evidence that real-time, continuous appliance-level information may be the most effective way to motivate behavior change [Corinna Fischer. Feedback on household electricity consumption: a tool for saving energy? Energy Efficiency, 1(1):79-104, February 2008].
Additionally, there are almost no studies on the human-computer interaction issues that such disaggregated datasets for the power consumption of buildings, as the ones that an implementation of NILM techniques would generate, would bring. How much information should be presented to the users of a facility? What is the appropriate way to display it? Which pieces of information are more effective for modifying behavior and reducing energy consumption?
Research projects where a number of different real-world buildings are being monitored using NILM are scarce, resulting in almost no scientific evaluation of the effectiveness of the technique for reducing energy consumption. Most of the literature draws its findings from controlled laboratory experiments, or one-time implementations on specific buildings. In 1997 the Electric Power Research Institute (EPRI) in California published a technical report [Electric Power Research Institute. Low-Cost NIALMS technology: Market issues & product assessment. Technical Report TR-108918-V1, Electric Power Research Institute, Palo Alto, Calif., September 1997] on the market feasibility of the NILM technology of the time, from the electric utility's perspective. In the report they conclude:                Project results indicate that NIALMS is a cost-effective load research tool for two-state appliances. However, for NIALMS to penetrate the mass market, reduction of per unit costs and enhancement of the algorithm to handle multi-state appliances is necessary. To achieve this, NIALMS functionality must be embedded in a low-cost electronic meter. For a robust offering to residential customers, a reliable NIALMS function also may be bundled with other services.        
The accompanying technical assessment report [Electric Power Research Institute. Low-Cost NIALMS technology: Technical assessment. Technical Report TR-108918-V2, Electric Power Research Institute, Palo Alto, Calif., November 1997] discusses some methods to resolve the issues. However, despite the fact that this was 12 years ago, there is still no important penetration into the market. More recently, in 2003, a report [Vernon A. Smith, Leslie Norford, and Steven Leeb. Final report compilation for equipment scheduling and cycling. Technical Report P-500-03-096-A2, California Energy Commission, October 2003] prepared for the California Energy Commission's Public Interest Energy Research (PIER) program, concluded that “there are issues regarding identification of multiple units of devices that are of the same make and model within a facility or on a branch circuit”. It also stated that the commercial value of NILM would still need to be determined. One of the sections of this report corresponds to a Ph.D. thesis [Kwangduk Douglas Lee. Electric load information system based on non-intrusive power monitoring. Thesis, Massachusetts Institute of Technology, 2003. Thesis Ph. D.—Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003] detailing all the improvements made to the technology up to that point, and supporting the technical recommendations for future improvements. Among the latter they mentioned: add the ability to monitor variable-power, constant-speed loads; and automate the training process “to the extent possible”.
Accordingly, there is a need for improved methods and apparatuses for monitoring energy consumption and for related operations, and particularly for monitoring of energy consumption in buildings and providing consumption awareness to users and building management systems. Those and other advantages of the present invention will be described in more detail herein.